"""simple docstring"""



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class  snake_case__      (       snake_case_       ):
 _snake_case       :					UNetaDModel
 _snake_case       :					ScoreSdeVeScheduler


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        super().__init__()
        self.register_modules(unet=lowerCamelCase    ,		scheduler=lowerCamelCase						)






 @torch.no_grad()
 def __call__( self    ,		lowerCamelCase = 1    ,		lowerCamelCase = 2000    ,		lowerCamelCase = None    ,		lowerCamelCase = "pil"    ,		lowerCamelCase = True    ,		**lowerCamelCase    ,		):
        __a       =    self.unet.config.sample_size
        __a       =    (batch_size, 3, img_size, img_size)

        __a       =    self.unet

        __a       =    randn_tensor(lowerCamelCase    ,		generator=lowerCamelCase						) * self.scheduler.init_noise_sigma
        __a       =    sample.to(self.device						)

        self.scheduler.set_timesteps(lowerCamelCase						)
        self.scheduler.set_sigmas(lowerCamelCase						)

        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
               __a       =    self.scheduler.sigmas[i] * torch.ones(shape[0]    ,		device=self.device						)

               # correction step
               for _ in range(self.scheduler.config.correct_steps						):
                      __a       =    self.unet(lowerCamelCase    ,		lowerCamelCase						).sample
                      __a       =    self.scheduler.step_correct(lowerCamelCase    ,		lowerCamelCase    ,		generator=lowerCamelCase						).prev_sample

               # prediction step
               __a       =    model(lowerCamelCase    ,		lowerCamelCase						).sample
               __a       =    self.scheduler.step_pred(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		generator=lowerCamelCase						)

               __a ,   __a       =    output.prev_sample, output.prev_sample_mean

        __a       =    sample_mean.clamp(0    ,		1						)
        __a       =    sample.cpu().permute(0    ,		2    ,		3    ,		1						).numpy()
        if output_type == "pil":
               __a       =    self.numpy_to_pil(lowerCamelCase						)

        if not return_dict:
               return (sample,)

        return ImagePipelineOutput(images=lowerCamelCase						)

"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
